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Computational requirements for control of the utah arm
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Gestures as Input: Neuroelectric Joysticks and Keyboards
IEEE Pervasive Computing
Toward subtle intimate interfaces for mobile devices using an EMG controller
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A continuous and objective evaluation of emotional experience with interactive play environments
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using a low-cost electroencephalograph for task classification in HCI research
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hand gestures for HCI using ICA of EMG
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Neuro-fuzzy control of a robotic exoskeleton with EMG signals
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EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Enabling always-available input: through on-body interfaces
CHI '09 Extended Abstracts on Human Factors in Computing Systems
A multi-level pressure-sensing two-handed interface with finger-mounted pressure sensors
Proceedings of Graphics Interface 2009
Enabling always-available input with muscle-computer interfaces
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Enhancing input on and above the interactive surface with muscle sensing
Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
Skinput: appropriating the body as an input surface
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Making muscle-computer interfaces more practical
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interactive optimization for steering machine classification
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Automatic recognition of sign language subwords based on portable accelerometer and EMG sensors
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Key-press gestures recognition and interaction based on SEMG signals
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Skinput: appropriating the skin as an interactive canvas
Communications of the ACM
Biofeedback game design: using direct and indirect physiological control to enhance game interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling and classification of sEMG based on blind identification theory
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
ERACLE: electromyography system for gesture interaction
HI'11 Proceedings of the 2011 international conference on Human interface and the management of information - Volume Part I
With a flick of the wrist: stretch sensors as lightweight input for mobile devices
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Proceedings of the Fifth International Conference on Body Area Networks
Muscle computer interfaces for driver distraction reduction
Computer Methods and Programs in Biomedicine
Design principles of hand gesture interfaces for microinteractions
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UISilk: towards interfacing the body
Proceedings of the second international workshop on Smart material interfaces: another step to a material future
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We explore the feasibility of muscle-computer interfaces (muCIs): an interaction methodology that directly senses and decodes human muscular activity rather than relying on physical device actuation or user actions that are externally visible or audible. As a first step towards realizing the mu-CI concept, we conducted an experiment to explore the potential of exploiting muscular sensing and processing technologies for muCIs. We present results demonstrating accurate gesture classification with an off-the-shelf electromyography (EMG) device. Specifically, using 10 sensors worn in a narrow band around the upper forearm, we were able to differentiate position and pressure of finger presses, as well as classify tapping and lifting gestures across all five fingers. We conclude with discussion of the implications of our results for future muCI designs.